Zhang et al. [1]} figured out that earlier methods drastically reduced their performance when applied to a new scene different from the ones present in the training set. So, they proposed to learn a mapping from the images to the counts and to adapt this mapping to new target scenes, performing cross-scene counting. To achieve this, they proposed a data-driven method where the network is fine-tuned using training samples that are considered similar to the target scene without using any extra-label information. Additionally, they introduced a new dataset, named WorldExpo’10, to evaluate cross-scene crowd counting. Inspired by this latter work, Walach and Wolf [2]} introduced a novel strategy based on layered boosting and selective sampling. Layered boosting involves iteratively adding cnn layers to the model such that every new layer is trained to estimate the residual error of the earlier prediction. For instance, after the first cnn layer is trained, the second cnn layer is trained on the difference between the estimation and the ground truth. This layered boosting approach is based on the notion of Gradient Boosting Machines (GBM) [3]}. The other contribution made by the authors is the use of a sample selection algorithm to improve the training process by reducing the effect of low-quality samples such as trivial samples or outliers. According to the authors, the samples that are correctly classified early on are trivial samples. Presenting such samples for training even after the networks have learned to classify them introduces bias in the network for such samples, thereby affecting its generalization performance. Another source of training inefficiency is the presence of outliers such as mislabeled samples. Apart from affecting the network’s performance, these samples increase the training time. To overcome this issue, such samples are removed from the training process for some epochs.
